Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learning

TL;DR

This paper presents a deep reinforcement learning framework for home energy management that dynamically incorporates individual user preferences, improving energy efficiency and user involvement in demand response programs.

Contribution

It introduces a novel multi-mode DRL-based HEMS that dynamically models consumer preferences, outperforming traditional optimization methods in efficiency and user adaptability.

Findings

Achieves near-optimal energy optimization performance.

Outperforms traditional MILP algorithms in computational efficiency.

Effectively incorporates dynamic, user-defined preferences.

Abstract

Home Energy Management Systems (HEMS) have emerged as a pivotal tool in the smart home ecosystem, aiming to enhance energy efficiency, reduce costs, and improve user comfort. By enabling intelligent control and optimization of household energy consumption, HEMS plays a significant role in bridging the gap between consumer needs and energy utility objectives. However, much of the existing literature construes consumer comfort as a mere deviation from the standard appliance settings. Such deviations are typically incorporated into optimization objectives via static weighting factors. These factors often overlook the dynamic nature of consumer behaviors and preferences. Addressing this oversight, our paper introduces a multi-mode Deep Reinforcement Learning-based HEMS (DRL-HEMS) framework, meticulously designed to optimize based on dynamic, consumer-defined preferences. Our primary goal is to augment consumer involvement in Demand Response (DR) programs by embedding dynamic multi-mode preferences tailored to individual appliances. In this study, we leverage a model-free, single-agent DRL algorithm to deliver a HEMS framework that is not only dynamic but also user-friendly. To validate its efficacy, we employed real-world data at 15-minute intervals, including metrics such as electricity price, ambient temperature, and appliances' power consumption. Our results show that the model performs exceptionally well in optimizing energy consumption within different preference modes. Furthermore, when compared to traditional algorithms based on Mixed-Integer Linear Programming (MILP), our model achieves nearly optimal performance while outperforming in computational efficiency.